Achieving Savings Through Reliability Using Real Time Data

Abstract

Historically, the drilling industry has utilized real time data (RTD) for improving drilling efficiencies, documenting occurred events, and reactive decision-making. RTD should promote situational awareness with respect to wellbore instability and hazards. Drilling trends identifiable in RTD change over time as abnormal trends leading to hazards and rarely occur instantaneously. These destabilizing events such as cuttings loading, barrier compromise, kicks, pack-offs, wellbore collapse, or other mechanical and pressure-related causes, whether in rotating or flat-time operations, can be avoided or mitigated. Accepting these hazards as a necessary outcome of drilling operations can be a dangerous proposition and denies risk accountability.Enhancing the systemic approach to process safety represents a paradigm shift in the industry and requires changing behaviors through proactively managing and controlling the pace of the drilling process. A new paradigm suggests the use of a deterministic, physics-based model of real time force-balance in the wellbore during the construction process. Identifying the trends of these forces become predictors of hazards. Mitigating and correcting the trends, in real time avoids process safety failures and consistently results in improvements in non-productive time (NPT) and removable lost time (RLT), notwithstanding the large associated costs. The drilling trend behaviors that predict hazards can facilitate leading as opposed to lagging Key Performance Indicators (KPIs). The new paradigm suggested enables hazards avoidance and thus significantly reduces the costs of drilling. This paper describes and explains these predictive techniques as well as offers suggestions for industry training with respect to surveillance and hazards avoidance using RTD simulators, much as airline pilots would use for continual training. 2 OTC-26935-MS • 12 days of Wasted Time, • $ 20,000,000 plus cost of sidetrack. 8 OTC-26935-MS

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

“…Meeker and Hong provide a strategic perspective on the potential impact of FRBD in reliability with a natural extension from traditional reliability methods and propose the ideas to enhance the impact of the statistical and reliability analysis based on FRBD [17,60]. Additionally, drilling industry has utilized FRBD to perform reliability analytics and prediction to improve drilling reliability, efficiencies, proactive, and reactive reliability decision-making [1,2,6,7,13,[61][62][63].…”

“…Increasingly, modern electronic development enables drilling tools to be designed with automatic signal and data tracking, collection, measurements, storage, and transmission functions for effective data analytics, conditional monitoring, and drilling control [4,[13][14][15]. A large variety of downhole drilling operation, environment, and dynamic data is captured, measured, and collected by multiple sophisticated sensors, transmitted to surface equipment, and instantaneously streamed to the data center for processing via surface acquisition systems.…”

“…Various variables are tracked and information is recorded at small time intervals, which provides periodic snapshots of downhole environments, drilling performance, and cumulative tool usage [16]. Surface equipment can process, decode, and interpret the downhole data or signals to analyze the real time downhole drilling operation status [7,13]. Unquestionably, drilling downhole data are available for tracking, analyzing, and predicting tool operational reliability, failure and lifetime, determining tool warranty-cost, and optimizing drilling process [17].…”